In the iterative binding module (\(\Phi _{\text{bind}}(\cdot )\) ),
we use two learnable query vectors (as we consider the case of segmenting a single moving object from the background), and choose \(T=5\)  iterations (as explained in Section REF ).
We adopt a simple VGG-style network for the CNN encoder and decoder, with 3 blocks each;
in each block, we use 2 sets of convolutions, instance normalization and ReLU activation.
We train with a batch size of 64 images and use
the Adam optimizer [1]} with an initial learning rate of \(5\times 10^{-4}\) ,
decreasing every 80k iterations.
The exact architecture description and training schedule can be found in the Supplementary Material,
and the submitted source code.
